WatchActive

Transit disruption in France ({count} services affected)

🇫🇷 France

Onset: 2026-07-13 · First detected: 2026-07-13T18:00:48.466537+00:00 · Last observed: 2026-07-14T06:42:04.266580+00:00

Generated at: 2026-07-14T06:42:04.266580+00:00 · The radar runs every 6 hours; data may lag by up to 6 hours.

Plain-language brief

What happened, who is affected, what similar past episodes looked like and what to do — every number links to its source.

What happened

ObservedOn 2026-07-13, our automated monitoring flagged a public-transport disruption in France — rated watch — the lowest of our three alert levels.[RATP Open Data Paris]
ObservedThe reading behind this alert: active messages at 3 (the full technical detail is in the sections below).[RATP Open Data Paris]

Who is affected

ObservedHotels, travel managers, OTAs and tour operators with guests in France during the event window are the most directly affected, together with travellers holding bookings there.[RATP Open Data Paris]

What similar past episodes looked like

Historical associationIn similar past high-disruption months, overnight stays in France averaged 22.4% below normal calm-month levels — a historical association from our published research panel, not a prediction.[estimates.historical_correlation]
Historical associationLooking at the 3 most similar months in our 2014-2026 research panel, overnight stays one month later differed from a year earlier by a median of +7.2% — again a historical association, not a prediction of this event.[estimates.analogues]

What to do now

RecommendationFor now, keep an eye on the situation in France; no change to bookings is needed yet.[detection.observed]

Impact

active_messages

Historical monthly context — not current conditionsAs of: 2026-05 · Disruption index: 44.86 · 2 months older than this event

How this was detected

Rule: transit_disruption_threshold

Method: deterministic-rule

Thresholds

abs_min_active	5
z_min	2

Observed

active_messages

Baseline

n_points	61
window_hours	680.27
mean	1.57
std	0.61

Sources

RATP Open Data Paris snapshotcollected: 2026-07-14T06:42:04.266580+00:00

Corroborating evidence

Independent live sources scanned for disruption-significant entries about this country. Only strict, disclosed rules count — routine entries are never counted.

meteoalarm-eumetnet1 matching entriesSnapshot
Rule: count the event country's Meteoalarm entry only when its max severity is Severe or Extreme with at least one active warning; Moderate/minor warning loads are routine
- FR — 360 — Extreme
viaggia-sicuri-it2 matching entriesSnapshot
Rule: count Viaggiare Sicuri 'ultimi avvisi' entries for the event country ONLY when the item's classified phenomenon (from its free-text title/excerpt) can corroborate the event type — a rail-strike or protest notice does not corroborate a seismic event; the feed only lists destinations with an active alert
- FRANCIA: allerta meteo. — Il Servizio Meteo locale segnala lo stato di allerta “canicola estrema” per Parigi, a partire dal mezzogiorno di sabato 11 luglio 2026: si invitano i connazionali residenti o
- FRANCIA: allerta meteo. — Le recenti condizioni meteorologiche estreme stanno interessando molte zone della Francia: si raccomanda di attenersi scrupolosamente alle indicazioni delle Autorità locali e

Sources scanned: auswaertiges-amt-api-de, bison-fute-france, emsc-seismic, france-diplomatie-travel, gdacs-live, mae-ro-travel, meteoalarm-eumetnet, minbuza-nl-travel, uk-fcdo-advisories, us-state-advisories, viaggia-sicuri-it

Estimates

Estimates are labeled model outputs derived from published historical artifacts — they are context, not forecasts.

Model output — historical correlation estimatehistorical-correlation

r_nights	0.40
r_arrivals	0.38
best_lag_months	6
best_lag_r	-0.45
baseline_deviation_pct	-22.37
n_months	136

Caveats:

correlation is association, not causation
baseline_deviation_pct is the observed average nights deviation during historical high-disruption months (index > 50) vs calm months
coefficients are dominated by the 2020-2021 pandemic period and may overstate the impact of smaller disruptions
computed on monthly national aggregates; sub-national and short-lived events are diluted

Observed history — analogue episodes, not a predictionanalogue-episodesindex_equiv = 25

Episode	score	index	h0	h1	h2	h3
🇫🇷 France · 2016-03	1.470	79	6.37	-5.80	-7.11	-4.05
🇫🇷 France · 2023-03	1.458	79	4.48	7.20	9.43	-0.13
🇫🇷 France · 2023-04	1.436	79	7.20	9.43	-0.13	-2.01

median_h1	7.20
median_h2	-0.13
median_h3	-2.01

Caveats:

analogues are historically similar months, not a forecast of this event
similarity is computed on a 10-feature panel embedding (tourism volumes, disruption/stringency indices, seasonality); it ignores event semantics
the index_equiv mapping from severity to disruption-index points is a disclosed modelling assumption, not an observation
observed_nights_yoy_pct mixes the event effect with everything else that happened in those months

Recommendations

Monitor the situation in France and re-check this report before confirming new bookings.
based on: detection.observed

For AI agents

Machine-readable JSON for this page:

/data/api/v1/events/ev-transit-disruption-fr-20260713-d8cbf2.json

Add as an MCP server (from the repo root):

claude mcp add travel-trends -- .venv/bin/python -m etl.mcp_server

API documentation→